research-article

Collaborative filtering using random neighbours in peer-to-peer networks

Authors:

Maarten van SteenAuthors Info & Claims

CNIKM '09: Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management

Pages 67 - 75

https://doi.org/10.1145/1651274.1651288

Published: 06 November 2009 Publication History

Abstract

Traditionally, collaborative filtering (CF) algorithms used for recommendation operate on complete knowledge. This makes these algorithms hard to employ in a decentralized context where not all users' ratings can be available at all locations. In this paper we investigate how the well-known neighbourhood-based CF algorithm by Herlocker et al. operates on partial knowledge; that is, how many similar users does the algorithm actually need to produce good recommendations for a given user, and how similar must those users be. We show for the popular MovieLens 1,000,000 and Jester datasets that sufficiently good recommendations can be made based on the ratings of a neighbourhood consisting of a relatively small number of randomly selected users.

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Cited By

Eichinger TKüpper A(2023)Distributed Data Minimization for Decentralized Collaborative Filtering SystemsProceedings of the 24th International Conference on Distributed Computing and Networking10.1145/3571306.3571400(140-149)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1145/3571306.3571400
Tang XXu HTan YGong Y(2021)Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing EnvironmentResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch035(751-769)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch035
Casino FPatsakis C(2020)An Efficient Blockchain-Based Privacy-Preserving Collaborative Filtering ArchitectureIEEE Transactions on Engineering Management10.1109/TEM.2019.294427967:4(1501-1513)Online publication date: Nov-2020
https://doi.org/10.1109/TEM.2019.2944279
Show More Cited By

Index Terms

Collaborative filtering using random neighbours in peer-to-peer networks
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Search engine architectures and scalability
      1. Distributed retrieval
      2. Peer-to-peer retrieval
  2. Information storage systems
    1. Storage architectures
      1. Distributed storage

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Published In

cover image ACM Conferences

CNIKM '09: Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management

November 2009

94 pages

ISBN:9781605588070

DOI:10.1145/1651274

General Chairs:
Jun Wang
University College London, UK
,
Shi Zhou
University College London, UK
,
Program Chair:
Dell Zhang
Birkbeck, University of London, UK

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2009

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November 6, 2009

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Cited By

Eichinger TKüpper A(2023)Distributed Data Minimization for Decentralized Collaborative Filtering SystemsProceedings of the 24th International Conference on Distributed Computing and Networking10.1145/3571306.3571400(140-149)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1145/3571306.3571400
Tang XXu HTan YGong Y(2021)Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing EnvironmentResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch035(751-769)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch035
Casino FPatsakis C(2020)An Efficient Blockchain-Based Privacy-Preserving Collaborative Filtering ArchitectureIEEE Transactions on Engineering Management10.1109/TEM.2019.294427967:4(1501-1513)Online publication date: Nov-2020
https://doi.org/10.1109/TEM.2019.2944279
Tang XXu HTan YGong Y(2017)Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing EnvironmentInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201707010212:3(11-27)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJITWE.2017070102
Zhou TChen LShen J(2017)Movie Recommendation System Employing the User-Based CF in Cloud Computing22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)10.1109/CSE-EUC.2017.194(46-50)Online publication date: Jul-2017
https://doi.org/10.1109/CSE-EUC.2017.194
Smirnov APonomarev A(2016)Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System ArchitectureEnterprise Information Systems10.1007/978-3-319-29133-8_22(455-475)Online publication date: 3-Feb-2016
https://doi.org/10.1007/978-3-319-29133-8_22
Smirnov APonomarev A(2014)A hybrid peer-to-peer recommendation system architecture based on locality-sensitive hashingProceedings of the 15th Conference of Open Innovations Association FRUCT10.1109/FRUCT.2014.6872418(119-125)Online publication date: 28-Apr-2014
https://dl.acm.org/doi/10.1109/FRUCT.2014.6872418
Ramezani MMoradi PAkhlaghian F(2014)A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domainsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2014.04.002408(72-84)Online publication date: Aug-2014
https://doi.org/10.1016/j.physa.2014.04.002
Moreno ACastro HRiveill M(2014)Client-Side Hybrid Rating Prediction for RecommendationUser Modeling, Adaptation, and Personalization10.1007/978-3-319-08786-3_33(369-380)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08786-3_33
Feng Xia Asabere NAhmed AJing Li Xiangjie Kong (2013)Mobile Multimedia Recommendation in Smart Communities: A SurveyIEEE Access10.1109/ACCESS.2013.22811561(606-624)Online publication date: 2013
https://doi.org/10.1109/ACCESS.2013.2281156
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